Abstract
MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and cognitive decline. While graph convolutional networks (GCNs) have emerged as popular tools for AD diagnosis due to their ability to handle structural information and fuse multi-modal features, deep learning approaches face significant challenges including the requirement for large datasets and sensitivity to unbalanced label distributions in AD research. To address these limitations and enhance the flexibility of GCNs, we propose a graph convolutional network based on the meta-learning paradigm (ADMGCN) for early AD diagnosis. This approach incorporates weighting and dimensionality reduction to improve performance, storage, and training efficiency. By leveraging meta-learning, we sample subjects to create numerous label-balanced tasks, maximizing data utilization and mitigating the impact of label imbalance. Additionally, the meta-learning framework enables rapid adaptation to new tasks and facilitates independent testing of the GCN. RESULTS: Our model, ADMGCN, was extensively validated on the Alzheimer's Disease Neuroimaging Initiative datasets. It achieved a maximum accuracy of 73.7% in the multi-classification task for early AD diagnosis. In three binary classification tasks, the model also demonstrated strong performance, achieving accuracies of 92.8%, 88.0%, and 79.6%, respectively. These results confirm that the proposed method provides an effective approach and worthwhile support for the early diagnosis of Alzheimer's disease. AVAILABILITY AND IMPLEMENTATION: ADMGCN is freely available at https://github.com/WendySun16/ADMGCN.